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DOC: consistent imports (GH9886) part II #10136

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24 changes: 11 additions & 13 deletions doc/source/10min.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,18 +6,16 @@
:suppress:

import numpy as np
import random
import pandas as pd
import os
np.random.seed(123456)
from pandas import options
import pandas as pd
np.set_printoptions(precision=4, suppress=True)
import matplotlib
try:
matplotlib.style.use('ggplot')
except AttributeError:
options.display.mpl_style = 'default'
options.display.max_rows=15
pd.options.display.mpl_style = 'default'
pd.options.display.max_rows = 15

#### portions of this were borrowed from the
#### Pandas cheatsheet
Expand Down Expand Up @@ -298,7 +296,7 @@ Using the :func:`~Series.isin` method for filtering:
.. ipython:: python

df2 = df.copy()
df2['E']=['one', 'one','two','three','four','three']
df2['E'] = ['one', 'one','two','three','four','three']
df2
df2[df2['E'].isin(['two','four'])]

Expand All @@ -310,7 +308,7 @@ by the indexes

.. ipython:: python

s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
s1
df['F'] = s1

Expand Down Expand Up @@ -359,7 +357,7 @@ returns a copy of the data.

.. ipython:: python

df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])
df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1.loc[dates[0]:dates[1],'E'] = 1
df1

Expand Down Expand Up @@ -409,9 +407,9 @@ In addition, pandas automatically broadcasts along the specified dimension.

.. ipython:: python

s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)
s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
s
df.sub(s,axis='index')
df.sub(s, axis='index')


Apply
Expand All @@ -431,7 +429,7 @@ See more at :ref:`Histogramming and Discretization <basics.discretization>`

.. ipython:: python

s = pd.Series(np.random.randint(0,7,size=10))
s = pd.Series(np.random.randint(0, 7, size=10))
s
s.value_counts()

Expand Down Expand Up @@ -516,9 +514,9 @@ See the :ref:`Grouping section <groupby>`
.. ipython:: python

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
df
Expand Down
110 changes: 52 additions & 58 deletions doc/source/advanced.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,15 +6,10 @@
:suppress:

import numpy as np
import random
np.random.seed(123456)
from pandas import *
options.display.max_rows=15
import pandas as pd
randn = np.random.randn
randint = np.random.randint
np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
from pandas.compat import range, zip
pd.options.display.max_rows=15

******************************
MultiIndex / Advanced Indexing
Expand Down Expand Up @@ -80,10 +75,10 @@ demo different ways to initialize MultiIndexes.
tuples = list(zip(*arrays))
tuples

index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
index

s = Series(randn(8), index=index)
s = pd.Series(np.random.randn(8), index=index)
s

When you want every pairing of the elements in two iterables, it can be easier
Expand All @@ -92,7 +87,7 @@ to use the ``MultiIndex.from_product`` function:
.. ipython:: python

iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
MultiIndex.from_product(iterables, names=['first', 'second'])
pd.MultiIndex.from_product(iterables, names=['first', 'second'])

As a convenience, you can pass a list of arrays directly into Series or
DataFrame to construct a MultiIndex automatically:
Expand All @@ -101,9 +96,9 @@ DataFrame to construct a MultiIndex automatically:

arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
s = Series(randn(8), index=arrays)
s = pd.Series(np.random.randn(8), index=arrays)
s
df = DataFrame(randn(8, 4), index=arrays)
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df

All of the ``MultiIndex`` constructors accept a ``names`` argument which stores
Expand All @@ -119,9 +114,9 @@ of the index is up to you:

.. ipython:: python

df = DataFrame(randn(3, 8), index=['A', 'B', 'C'], columns=index)
df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
df
DataFrame(randn(6, 6), index=index[:6], columns=index[:6])
pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])

We've "sparsified" the higher levels of the indexes to make the console output a
bit easier on the eyes.
Expand All @@ -131,7 +126,7 @@ tuples as atomic labels on an axis:

.. ipython:: python

Series(randn(8), index=tuples)
pd.Series(np.random.randn(8), index=tuples)

The reason that the ``MultiIndex`` matters is that it can allow you to do
grouping, selection, and reshaping operations as we will describe below and in
Expand Down Expand Up @@ -282,16 +277,16 @@ As usual, **both sides** of the slicers are included as this is label indexing.
def mklbl(prefix,n):
return ["%s%s" % (prefix,i) for i in range(n)]

miindex = MultiIndex.from_product([mklbl('A',4),
mklbl('B',2),
mklbl('C',4),
mklbl('D',2)])
micolumns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
('b','foo'),('b','bah')],
names=['lvl0', 'lvl1'])
dfmi = DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),
index=miindex,
columns=micolumns).sortlevel().sortlevel(axis=1)
miindex = pd.MultiIndex.from_product([mklbl('A',4),
mklbl('B',2),
mklbl('C',4),
mklbl('D',2)])
micolumns = pd.MultiIndex.from_tuples([('a','foo'),('a','bar'),
('b','foo'),('b','bah')],
names=['lvl0', 'lvl1'])
dfmi = pd.DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),
index=miindex,
columns=micolumns).sortlevel().sortlevel(axis=1)
dfmi

Basic multi-index slicing using slices, lists, and labels.
Expand Down Expand Up @@ -418,9 +413,9 @@ instance:

.. ipython:: python

midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
labels=[[1,1,0,0],[1,0,1,0]])
df = DataFrame(randn(4,2), index=midx)
midx = pd.MultiIndex(levels=[['zero', 'one'], ['x','y']],
labels=[[1,1,0,0],[1,0,1,0]])
df = pd.DataFrame(np.random.randn(4,2), index=midx)
df
df2 = df.mean(level=0)
df2
Expand Down Expand Up @@ -471,7 +466,7 @@ labels will be sorted lexicographically!
.. ipython:: python

import random; random.shuffle(tuples)
s = Series(randn(8), index=MultiIndex.from_tuples(tuples))
s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples))
s
s.sortlevel(0)
s.sortlevel(1)
Expand Down Expand Up @@ -509,13 +504,13 @@ an exception. Here is a concrete example to illustrate this:
.. ipython:: python

tuples = [('a', 'a'), ('a', 'b'), ('b', 'a'), ('b', 'b')]
idx = MultiIndex.from_tuples(tuples)
idx = pd.MultiIndex.from_tuples(tuples)
idx.lexsort_depth

reordered = idx[[1, 0, 3, 2]]
reordered.lexsort_depth

s = Series(randn(4), index=reordered)
s = pd.Series(np.random.randn(4), index=reordered)
s.ix['a':'a']

However:
Expand All @@ -540,15 +535,15 @@ index positions. ``take`` will also accept negative integers as relative positio

.. ipython:: python

index = Index(randint(0, 1000, 10))
index = pd.Index(np.random.randint(0, 1000, 10))
index

positions = [0, 9, 3]

index[positions]
index.take(positions)

ser = Series(randn(10))
ser = pd.Series(np.random.randn(10))

ser.iloc[positions]
ser.take(positions)
Expand All @@ -558,7 +553,7 @@ row or column positions.

.. ipython:: python

frm = DataFrame(randn(5, 3))
frm = pd.DataFrame(np.random.randn(5, 3))

frm.take([1, 4, 3])

Expand All @@ -569,11 +564,11 @@ intended to work on boolean indices and may return unexpected results.

.. ipython:: python

arr = randn(10)
arr = np.random.randn(10)
arr.take([False, False, True, True])
arr[[0, 1]]

ser = Series(randn(10))
ser = pd.Series(np.random.randn(10))
ser.take([False, False, True, True])
ser.ix[[0, 1]]

Expand All @@ -583,14 +578,14 @@ faster than fancy indexing.

.. ipython::

arr = randn(10000, 5)
arr = np.random.randn(10000, 5)
indexer = np.arange(10000)
random.shuffle(indexer)

timeit arr[indexer]
timeit arr.take(indexer, axis=0)

ser = Series(arr[:, 0])
ser = pd.Series(arr[:, 0])
timeit ser.ix[indexer]
timeit ser.take(indexer)

Expand All @@ -608,10 +603,9 @@ setting the index of a ``DataFrame/Series`` with a ``category`` dtype would conv

.. ipython:: python

df = DataFrame({'A' : np.arange(6),
'B' : Series(list('aabbca')).astype('category',
categories=list('cab'))
})
df = pd.DataFrame({'A': np.arange(6),
'B': list('aabbca')})
df['B'] = df['B'].astype('category', categories=list('cab'))
df
df.dtypes
df.B.cat.categories
Expand Down Expand Up @@ -669,18 +663,18 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.

.. code-block:: python

In [10]: df3 = DataFrame({'A' : np.arange(6),
'B' : Series(list('aabbca')).astype('category',
categories=list('abc'))
}).set_index('B')
In [9]: df3 = pd.DataFrame({'A' : np.arange(6),
'B' : pd.Series(list('aabbca')).astype('category')})

In [11]: df3 = df3.set_index('B')

In [11]: df3.index
Out[11]:
CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'],
categories=[u'a', u'b', u'c'],
ordered=False)

In [12]: pd.concat([df2,df3]
In [12]: pd.concat([df2, df3]
TypeError: categories must match existing categories when appending

.. _indexing.float64index:
Expand All @@ -705,9 +699,9 @@ same.

.. ipython:: python

indexf = Index([1.5, 2, 3, 4.5, 5])
indexf = pd.Index([1.5, 2, 3, 4.5, 5])
indexf
sf = Series(range(5),index=indexf)
sf = pd.Series(range(5), index=indexf)
sf

Scalar selection for ``[],.ix,.loc`` will always be label based. An integer will match an equal float index (e.g. ``3`` is equivalent to ``3.0``)
Expand Down Expand Up @@ -749,17 +743,17 @@ In non-float indexes, slicing using floats will raise a ``TypeError``

.. code-block:: python

In [1]: Series(range(5))[3.5]
In [1]: pd.Series(range(5))[3.5]
TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index)

In [1]: Series(range(5))[3.5:4.5]
In [1]: pd.Series(range(5))[3.5:4.5]
TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)

Using a scalar float indexer will be deprecated in a future version, but is allowed for now.

.. code-block:: python

In [3]: Series(range(5))[3.0]
In [3]: pd.Series(range(5))[3.0]
Out[3]: 3

Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat
Expand All @@ -768,12 +762,12 @@ example be millisecond offsets.

.. ipython:: python

dfir = concat([DataFrame(randn(5,2),
index=np.arange(5) * 250.0,
columns=list('AB')),
DataFrame(randn(6,2),
index=np.arange(4,10) * 250.1,
columns=list('AB'))])
dfir = pd.concat([pd.DataFrame(np.random.randn(5,2),
index=np.arange(5) * 250.0,
columns=list('AB')),
pd.DataFrame(np.random.randn(6,2),
index=np.arange(4,10) * 250.1,
columns=list('AB'))])
dfir

Selection operations then will always work on a value basis, for all selection operators.
Expand Down
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